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Introduction

We are interested in the topic of global economy because by analyzing global economic data, we will be able to better understand the complexities of global activity. Since the economy is something that affects everyone at all stages of their life, we felt that analyzing this topic would be beneficial for learning how the economy is changing, and why these changes are happening. We chose to analyze how (main questions asked here). The dataset we used to answer our questions was data published by World Bank and compiled from officially-recognized international resources. We then filtered the dataset to only include factors of interest.

Summary Information

While analyzing our data, we found some noteworthy observations, especially in regards to. will add on

Table

## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
country Average_Income Average_Population Average_Primary_Students Average_Secondary_Students Average_Female_Employment Average_Male_Employment Average_CO2_Emissions
Full country name Current USD Number of people Number of students in primary education Number of students in secondary education Employment to population ratio, female of age 15+ Employment to population ratio, male of age 15+ Metric tons per capita
United States 35443.5933703704 292988563.214286 24866699.095 24535708.67 54.2597583896552 67.4916206875862 18.7668335441667
United Kingdom 29695.8097414815 81783471.9285714 3336440.18518519 7863858.418 51.4568619565517 64.4875519844828 8.67450474866667
Germany 29173.8250740741 145312593.321429 6300313.32 12202867 47.3552072465517 62.7525517689655 10.007736653
Australia 27952.0206874074 25847180.1071429 3395859.375 2008642.22727273 52.861827587931 67.5584138013793 16.7741631741667
France 27597.0210725926 62862997.5714286 4064026.25925926 5898663.7037037 44.0714482268965 56.6534483024138 5.815734405375
Canada 27452.9253477778 105758229.821429 14650170.9259259 10134869.7407407 55.5336548368966 65.9722758324138 16.255029285
New Zealand 20587.8960228889 6534456.92857143 593517.888888889 174507 55.6787238282759 69.6753795224138 7.85257380008333
Trinidad and Tobago 8162.042121 1301275.71428571 162431.842105263 100239 47.0640348241379 72.3941726682759 24.4207555791667
Argentina 7299.09001014815 41971420.7857143 4879024.19230769 4115215.44 42.7301722558621 67.1047936810345 4.05062890175
Mexico 6071.7346497037 60987038.4642857 4566309.40909091 5370440.657 38.8386208103448 78.4519997955172 4.08559662541667
Venezuela, RB 5939.62003491667 27437690.5 3925834.64285714 2379958.96428571 42.8274140193103 71.3716901575862 6.34894336545833
Turkey 5775.81202655556 67704438.5 6388259.08 7021971.2 25.8247240003448 66.1830343051724 3.503067205375
Brazil 5519.66987874074 7350192.75 1141636.08 555996.461538462 45.4544138406897 72.4491379841379 1.88771675908333
Russian Federation 5212.6710262 470543.357142857 79373.9545454545 21009.1428571429 51.1679658562069 64.6710343193104 11.4839522378261
South Africa 3889.72430459259 47799352.25 7450077.66666667 4543342.9047619 30.7891380703448 48.9859999296552 8.79587839325
Dominican Republic 3575.48389488889 9003738.71428571 1256906.33333333 805466.45 38.1561722596552 73.9891725737931 2.08636407683333
Cuba 3456.1048162 11136884.8571429 903673.25 830083.62962963 36.7899310672414 65.2815518875862 2.6214346615
Colombia 3335.76092696296 183216830.035714 18245755.173913 24094869.0666667 46.3114483275862 74.8088962951724 1.60712000575
Jamaica 3314.571193 2713364.35714286 300872.380952381 236080.05 48.0172069158621 67.3635866889655 3.43782031816667
Peru 2943.42244925926 38714254.8214286 4919608.25 3794783.70833333 59.3028618386207 78.5110689351724 1.29846037733333
Fiji 2937.20800766667 4106439.28571429 350967.296296296 467328.487874074 37.0551034331034 75.5377931262069 1.16624691070833
China 2317.49492967778 1289438392.85714 112416740.681818 82276024.64 65.0590337555172 77.0650345365517 4.25750755808333
Guatemala 1980.97117617037 32274739.8214286 2318994.34782609 2568202.95454545 39.1888964431034 83.4268275289655 0.802869573375
Egypt, Arab Rep. 1567.29896805926 75884896.9285714 9185029.68 7402791.81818182 16.5156552868966 67.9244487365517 2.00329672666667
Indonesia 1399.77769218148 225523340.857143 29538986.7857143 17236218.0714286 46.7152069355172 78.8442411751724 1.506025673125
Honduras 1283.94670935185 13097557.3214286 2083115.22222222 775975.961538462 42.4835518148276 83.1546546672414 0.853342292541667
Nigeria 1115.31100466296 140709456.607143 20149234.2608696 7699497.64705882 51.2226896610345 60.7360688562069 0.545409081
Papua New Guinea 983.382141114815 20615710.8571429 1933546 2360635.625 57.699586407931 58.9785169406897 0.58609814725
Solomon Islands 885.392906118519 822878.571428571 111972.181818182 97326.8 80.9178282644828 83.7860691455172 0.375734327041667
Pakistan 741.757267003704 159446862 17951283.2631579 9297347.66666667 18.0434483496552 81.0362072648276 0.790770380416667
India 741.168353740741 1132564205.85714 123750290.958333 94937446.28 26.0148621917241 76.8905173989655 1.10151404641667
Bangladesh 608.54765687037 135813155 16630850.625 11708118.25 27.4166896424138 82.1145864027586 0.280639762375
Haiti 514.402058251852 9137952.46428571 1203846.42857143 NaN 51.0486551489655 64.924344952069 0.184700951291667
Ethiopia 213.765425175556 76844164.2142857 8298188.41666667 2349818.14285714 69.1881719127586 87.0633444944828 0.0680737755416667
Congo, Dem. Rep. 205.667087147083 56420915.25 8313774.58823529 2975326.69230769 65.1650685293103 67.4212070337931 0.0390856565833333

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## Chart 3